Metabolomic Biomarker Identification in Presence of Outliers and Missing Values
Joint Authors
Kumar, Nishith
Hoque, Md. Aminul
Shahjaman, Md.
Islam, S. M. Shahinul
Mollah, Md. Nurul Haque
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes.
For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy.
Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers.
However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly.
Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification.
Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset.
We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers.
Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease.
American Psychological Association (APA)
Kumar, Nishith& Hoque, Md. Aminul& Shahjaman, Md.& Islam, S. M. Shahinul& Mollah, Md. Nurul Haque. 2017. Metabolomic Biomarker Identification in Presence of Outliers and Missing Values. BioMed Research International،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1134764
Modern Language Association (MLA)
Kumar, Nishith…[et al.]. Metabolomic Biomarker Identification in Presence of Outliers and Missing Values. BioMed Research International No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1134764
American Medical Association (AMA)
Kumar, Nishith& Hoque, Md. Aminul& Shahjaman, Md.& Islam, S. M. Shahinul& Mollah, Md. Nurul Haque. Metabolomic Biomarker Identification in Presence of Outliers and Missing Values. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1134764
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134764